Current deployment status
AI software that works inside your operations, not alongside them
We build AI software that plugs directly into the workflows your team already runs. No parallel systems, no migration headaches. The models we ship read your data, learn your edge cases, and surface the signals your analysts would catch in six hours — in under six seconds. Right now we maintain active deployments across logistics scheduling, financial document parsing, and customer-intent classification for organisations ranging from 40-person specialist firms to enterprise divisions with 2,000+ seats.
Is AI software the right move for your team?
Not every problem needs a neural network. Sometimes a well-written SQL query or a spreadsheet macro does the job. We start every engagement with a diagnostic call — typically 45 minutes — where we map your current bottleneck, estimate the data volume involved, and tell you honestly whether an AI approach will pay for itself within 14 months. If it won't, we say so. Around a third of diagnostic calls end with us recommending a simpler path, and that recommendation is free.
The table to the right shows the signals we look for. If two or more of these apply, a conversation is worth your time.
| Signal | Present? |
|---|---|
| Manual data review takes more than 10 hours per week | ✓ strong fit |
| Decision rules change quarterly or faster | ✓ strong fit |
| Structured data exists but is under-used | ✓ strong fit |
| Team size is under 5 people | ✗ may not justify cost |
| Data lives in paper-only formats | ⚠ possible with OCR pipeline |
What we build
Classification engines
Document triage, support-ticket routing, lead scoring. We train on your labelled examples and deliver a containerised model your dev team can call via REST. Typical accuracy after two training rounds: 91–96% depending on label quality.
Forecasting models
Demand planning, churn prediction, inventory reorder timing. We blend time-series methods with gradient-boosted trees, tuned against your historical data. Forecast horizon ranges from 7 days to 18 months depending on the domain.
Automation pipelines
End-to-end workflows that ingest raw data, clean it, run inference, and push results to your dashboard or ERP. We handle scheduling, retry logic, and alerting so your team only sees the output — not the plumbing.
From the field
We handed them 14 months of shipping manifests and got back a routing model that cut our late-delivery rate from 11% to under 3%. The whole thing went live in nine weeks. Our ops manager still talks about it.— Head of distribution, a Midlands-based fulfilment company (under NDA)
This deployment involved a custom gradient-boosted model trained on 380,000 shipment records. Retraining runs monthly on fresh data.
How an engagement unfolds
Diagnostic call
45 minutes. We map the bottleneck, review data availability, and give you a candid assessment. No charge, no commitment.
Scoping document
Within five working days you receive a written scope: what we'll build, which data we need, expected accuracy range, timeline, and a fixed price. Typical projects land between £8,000 and £45,000.
Build and validate
We train models against your data, run blind-test evaluations, and iterate until performance meets the agreed threshold. You see results in a shared dashboard throughout.
Deploy and monitor
We containerise the solution, integrate it with your stack, and set up monitoring alerts. Post-launch support runs for 90 days at no extra cost. After that, optional retainer plans start at £600/month.
Choosing the right engagement model
We offer three engagement shapes. The right one depends on how much internal AI expertise you already have and whether you need ongoing model management or a one-off build.
| Feature | Project build | Embedded sprint | Managed service |
|---|---|---|---|
| Fixed-price delivery | ✓ | ✗ | ✗ |
| Your team learns to maintain the model | ✗ | ✓ | ✗ |
| Ongoing retraining included | ✗ | ✗ | ✓ |
| Typical duration | 6–12 weeks | 2–4 weeks | Rolling monthly |
| Best for | Defined problem, clear data | Teams building internal capability | Organisations without ML staff |
Why most AI projects fail (and how ours don't)
The failure rate for enterprise AI initiatives hovers around 80%, according to Gartner's 2023 survey. That number is real, and it's mostly caused by three things: vague problem definitions, poor data hygiene, and models that never leave the notebook.
We sidestep the first by refusing to start without a measurable success criterion. "Make our process smarter" is not a brief. "Reduce false-positive fraud alerts from 34% to under 12%" is. Every scoping document we write contains a pass/fail metric agreed before a single line of training code runs.
Data hygiene is harder. We spend roughly 40% of every project budget on cleaning, validating, and augmenting training data. That ratio surprises clients who expect the modelling to be the expensive part. It isn't. Garbage labels produce garbage predictions, and no architecture change fixes that.
Deployment is where the last wave of failures happens. A Jupyter notebook is not a product. We ship Docker containers with health-check endpoints, structured logging, and automated rollback triggers. If a retrained model performs worse than the previous version on a held-out test set, the deployment stops and an alert fires. No human has to catch it.
They told us our labelling was the bottleneck, not the model. Turned out they were right — after a two-week data audit, accuracy jumped 14 points without changing the architecture at all.
— CTO, a London fintech (series B)Common questions before you commit
We've grouped the questions we hear most often. If yours isn't here, the inquiry form below reaches us directly and we typically reply within one working day.
How much data do we need before an AI approach is viable?
Can you work with on-premise data that can't leave our network?
What happens if the model underperforms after launch?
Do you handle GDPR compliance for the data pipeline?
What tech stack do you typically use?
Start with a diagnostic call
Fill in the short form and we'll arrange a 45-minute call within two working days. No pitch deck, no sales follow-up chain — just a direct conversation about your data and whether AI software makes sense for your situation.
Thank you. We'll be in touch within two working days.